mlsanityData to 7 Oct 2026

Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach

arXiv 2202.12797 ↗First posted 25 Feb 2022cs.LG cs.GT math.OCcited by 5

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Abstract (arXiv)

Dynamic mechanism design studies how mechanism designers should allocate resources among agents in a time-varying environment. We consider the problem where the agents interact with the mechanism designer according to an unknown Markov Decision Process (MDP), where agent rewards and the mechanism designer's state evolve according to an episodic MDP with unknown reward functions and transition kernels. We focus on the online setting with linear function approximation and propose novel learning algorithms to recover the dynamic Vickrey-Clarke-Grove (VCG) mechanism over multiple rounds of interaction. A key contribution of our approach is incorporating reward-free online Reinforcement Learning (RL) to aid exploration over a rich policy space to estimate prices in the dynamic VCG mechanism. We show that the regret of our proposed method is upper bounded by tildeO(T2/3) and further devise a lower bound to show that our algorithm is efficient, incurring the same Omega(T2 / 3) regret as the lower bound, where T is the total number of rounds. Our work establishes the regret guarantee for online RL in solving dynamic mechanism design problems without prior knowledge of the underlying model.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 12 of its 43 references that are in the map. First posted on arXiv 25 Feb 2022.

Builds on

12 of 43 references.

References · 12

  1. 2020Is Pessimism Provably Efficient for Offline RL?cited by 362
  2. 2020Model-Based Reinforcement Learning with Value-Targeted Regressioncited by 233
  3. 2019Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Boundcited by 199
  4. 2020Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processescited by 199
  5. 2020Provable Self-Play Algorithms for Competitive Reinforcement Learningcited by 112
  6. 2020Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mappingcited by 102
  7. 2020Fast active learning for pure exploration in reinforcement learningcited by 92
  8. 2020On Reward-Free Reinforcement Learning with Linear Function Approximationcited by 89
  9. 2020Provably Efficient Reward-Agnostic Navigation with Linear Value Iterationcited by 56
  10. 2020Nonstationary Reinforcement Learning with Linear Function Approximationcited by 23
  11. 2021On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Gamecited by 20
  12. 2020Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learningcited by 11

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1.

5 papers.

  1. 2023PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedbackcited by 38
  2. 2024STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Makingcited by 22
  3. 2023Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Modelscited by 7
  4. 2022Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learningcited by 4
  5. 2025Online Learning for Dynamic Vickrey-Clarke-Groves Mechanism in Unknown Environmentscited by 0